Launching a new fintech product required certainty across a complex microservices platform. With Causely modeling cause-and-effect relationships across services, Humm Group gained system-level understanding and confidence that critical dependencies behaved correctly during launch.
Reliability is managed in services, but users experience outcomes. In complex, multi-service and AI-driven architectures, systems can look healthy in isolation while end-to-end workflows still fail. Product reliability needs visibility at the level of transactions and flows.
Alerts are signals, not explanations. By explicitly mapping alerts to symptoms and inferred root causes, Causely turns alert noise into a coherent explanation of what is actually happening in the system.
Slow SQL queries degrade UX and reliability. This guide shows how to distill OpenTelemetry DB spans into actionable metrics: build span-derived slow-query dashboards, rank queries by traffic impact, and detect regressions with anomaly baselines, so you fix what matters first. Hands-on lab included.
Causely’s causal model has been expanded for asynchronous messaging systems. Instead of treating queues as opaque buffers, Causely models messaging infrastructure as it operates in production, making asynchronous failures explicit and explainable.
Alerts are supposed to start an investigation. Too often, they start translation: what is the system doing right now? That translation slows containment, splinters context, and stretches customer impact.
Asynchronous pipelines sit at the core of most modern systems. Message brokers accept traffic, consumers process it in the background, and downstream services depend on the results. When these systems fail, the failure rarely shows up where it starts.
Originally published to the Slight Reliability Podcast.
Causely’s expanded Datadog integration turns Datadog APM signals into system-level causal intelligence, helping teams understand how issues propagate across services and pinpoint true root cause.
How Causely uses FluxCD and GitOps to ship weekly on Kubernetes, keep clusters in sync, and wire up OpenTelemetry and Causely in a hands-on lab you can copy.
Gartner recognized Causely for maintaining a live causality graph and using continuous inference to identify the underlying driver behind changes in golden signals as they emerge, even when failures cascade across multiple services.
In a 50 to 100+ microservice environment with dense service-to-service dependencies, even small regressions can cascade silently. And slowing down isn’t an option. Leadership needs faster delivery and fewer incidents. This is why we built Reliability Delta.